Overview

Dataset statistics

Number of variables16
Number of observations274
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.4 KiB
Average record size in memory128.5 B

Variable types

NUM16

Warnings

W_FEMALE is highly correlated with AA_FEMALE and 4 other fieldsHigh correlation
AA_FEMALE is highly correlated with W_FEMALE and 4 other fieldsHigh correlation
MALE is highly correlated with FEMALEHigh correlation
FEMALE is highly correlated with MALEHigh correlation
AA_MALE is highly correlated with AA_FEMALE and 4 other fieldsHigh correlation
W_MALE is highly correlated with AA_FEMALE and 4 other fieldsHigh correlation
H_MALE is highly correlated with H_Female and 1 other fieldsHigh correlation
H_Female is highly correlated with H_MALE and 1 other fieldsHigh correlation
AA is highly correlated with AA_FEMALE and 4 other fieldsHigh correlation
H is highly correlated with H_Female and 1 other fieldsHigh correlation
W is highly correlated with AA_FEMALE and 4 other fieldsHigh correlation
df_index has unique values Unique
ECONOMICALLY_DISADVANTAGED has unique values Unique
AA has unique values Unique
H_Female has 3 (1.1%) zeros Zeros
AA_FEMALE has 7 (2.6%) zeros Zeros
W_FEMALE has 4 (1.5%) zeros Zeros
AA_MALE has 6 (2.2%) zeros Zeros
W_MALE has 4 (1.5%) zeros Zeros

Reproduction

Analysis started2020-11-09 15:22:22.927035
Analysis finished2020-11-09 15:23:04.573594
Duration41.65 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct274
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160.7664234
Minimum0
Maximum326
Zeros1
Zeros (%)0.4%
Memory size2.1 KiB
2020-11-09T09:23:04.679596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.65
Q184.5
median158.5
Q3233.75
95-th percentile308.35
Maximum326
Range326
Interquartile range (IQR)149.25

Descriptive statistics

Standard deviation93.42704057
Coefficient of variation (CV)0.5811352807
Kurtosis-1.110136404
Mean160.7664234
Median Absolute Deviation (MAD)75
Skewness0.04738561112
Sum44050
Variance8728.611909
MonotocityStrictly increasing
2020-11-09T09:23:04.842596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
32610.4%
 
11410.4%
 
10610.4%
 
10710.4%
 
11010.4%
 
11110.4%
 
11210.4%
 
11310.4%
 
11510.4%
 
8410.4%
 
Other values (264)26496.4%
 
ValueCountFrequency (%) 
010.4%
 
110.4%
 
210.4%
 
310.4%
 
510.4%
 
ValueCountFrequency (%) 
32610.4%
 
32410.4%
 
32010.4%
 
31910.4%
 
31810.4%
 

grad_rate
Real number (ℝ≥0)

Distinct31
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9193430657
Minimum0.6
Maximum0.99
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-11-09T09:23:05.001600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile0.793
Q10.91
median0.94
Q30.96
95-th percentile0.98
Maximum0.99
Range0.39
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.06428763637
Coefficient of variation (CV)0.06992779819
Kurtosis6.034400229
Mean0.9193430657
Median Absolute Deviation (MAD)0.03
Skewness-2.207208413
Sum251.9
Variance0.00413290019
MonotocityNot monotonic
2020-11-09T09:23:05.146594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%) 
0.963412.4%
 
0.943312.0%
 
0.922810.2%
 
0.97279.9%
 
0.93217.7%
 
0.98196.9%
 
0.95196.9%
 
0.91176.2%
 
0.9145.1%
 
0.88103.6%
 
Other values (21)5219.0%
 
ValueCountFrequency (%) 
0.610.4%
 
0.6410.4%
 
0.6510.4%
 
0.6610.4%
 
0.6920.7%
 
ValueCountFrequency (%) 
0.9982.9%
 
0.98196.9%
 
0.97279.9%
 
0.963412.4%
 
0.95196.9%
 

TOTAL
Real number (ℝ≥0)

Distinct258
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean899.0510949
Minimum51
Maximum2295
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-11-09T09:23:05.329594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile292.5
Q1495.5
median781
Q31209.5
95-th percentile1975.35
Maximum2295
Range2244
Interquartile range (IQR)714

Descriptive statistics

Standard deviation529.0765169
Coefficient of variation (CV)0.5884832574
Kurtosis-0.003577587333
Mean899.0510949
Median Absolute Deviation (MAD)336.5
Skewness0.8382575572
Sum246340
Variance279921.9607
MonotocityNot monotonic
2020-11-09T09:23:05.489593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
166720.7%
 
70720.7%
 
91420.7%
 
131320.7%
 
156620.7%
 
40720.7%
 
81820.7%
 
48820.7%
 
53320.7%
 
84720.7%
 
Other values (248)25492.7%
 
ValueCountFrequency (%) 
5110.4%
 
6710.4%
 
7010.4%
 
8810.4%
 
10910.4%
 
ValueCountFrequency (%) 
229510.4%
 
228810.4%
 
228710.4%
 
228010.4%
 
227010.4%
 

ECONOMICALLY_DISADVANTAGED
Real number (ℝ≥0)

UNIQUE

Distinct274
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.300536294
Minimum0.007798440312
Maximum0.7301854975
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-11-09T09:23:05.662594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.007798440312
5-th percentile0.07012489762
Q10.2160937819
median0.2848396719
Q30.3726889426
95-th percentile0.6004016121
Maximum0.7301854975
Range0.7223870572
Interquartile range (IQR)0.1565951607

Descriptive statistics

Standard deviation0.1439897892
Coefficient of variation (CV)0.4791094854
Kurtosis0.8521391807
Mean0.300536294
Median Absolute Deviation (MAD)0.08037259227
Skewness0.6724567369
Sum82.34694454
Variance0.02073305938
MonotocityNot monotonic
2020-11-09T09:23:05.832597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.333333333310.4%
 
0.257894736810.4%
 
0.334467120210.4%
 
0.45195195210.4%
 
0.386699507410.4%
 
0.258527827610.4%
 
0.664015904610.4%
 
0.287356321810.4%
 
0.0602409638610.4%
 
0.378151260510.4%
 
Other values (264)26496.4%
 
ValueCountFrequency (%) 
0.00779844031210.4%
 
0.0106681639510.4%
 
0.0155709342610.4%
 
0.0250696378810.4%
 
0.0286951813810.4%
 
ValueCountFrequency (%) 
0.730185497510.4%
 
0.709415584410.4%
 
0.709327548810.4%
 
0.706967213110.4%
 
0.702064896810.4%
 

FEMALE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct270
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4866550785
Minimum0.3769230769
Maximum0.6342592593
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-11-09T09:23:06.325597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.3769230769
5-th percentile0.4494149496
Q10.470782921
median0.4848192924
Q30.4995584072
95-th percentile0.5255219019
Maximum0.6342592593
Range0.2573361823
Interquartile range (IQR)0.02877548616

Descriptive statistics

Standard deviation0.02781375505
Coefficient of variation (CV)0.05715291236
Kurtosis5.813975732
Mean0.4866550785
Median Absolute Deviation (MAD)0.01430102395
Skewness0.8353810051
Sum133.3434915
Variance0.0007736049702
MonotocityNot monotonic
2020-11-09T09:23:06.493598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.482142857131.1%
 
0.520.7%
 
0.521739130420.7%
 
0.522388059710.4%
 
0.478458049910.4%
 
0.475644699110.4%
 
0.498820754710.4%
 
0.494230769210.4%
 
0.491695804210.4%
 
0.493553008610.4%
 
Other values (260)26094.9%
 
ValueCountFrequency (%) 
0.376923076910.4%
 
0.392670157110.4%
 
0.395498392310.4%
 
0.432360742710.4%
 
0.441458733210.4%
 
ValueCountFrequency (%) 
0.634259259310.4%
 
0.607843137310.4%
 
0.596899224810.4%
 
0.590909090910.4%
 
0.550774526710.4%
 

H_Female
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct267
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0317168896
Minimum0
Maximum0.1456456456
Zeros3
Zeros (%)1.1%
Memory size2.1 KiB
2020-11-09T09:23:06.668595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004916775423
Q10.01225668271
median0.02237745799
Q30.04139505921
95-th percentile0.09101435569
Maximum0.1456456456
Range0.1456456456
Interquartile range (IQR)0.0291383765

Descriptive statistics

Standard deviation0.02855525981
Coefficient of variation (CV)0.9003171552
Kurtosis2.85184275
Mean0.0317168896
Median Absolute Deviation (MAD)0.01261517554
Skewness1.68160521
Sum8.690427749
Variance0.000815402863
MonotocityNot monotonic
2020-11-09T09:23:06.833597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
031.1%
 
0.00990099009931.1%
 
0.00502512562820.7%
 
0.0106100795820.7%
 
0.00375234521620.7%
 
0.0261194029910.4%
 
0.020396270410.4%
 
0.128672745710.4%
 
0.0176870748310.4%
 
0.00675675675710.4%
 
Other values (257)25793.8%
 
ValueCountFrequency (%) 
031.1%
 
0.00211864406810.4%
 
0.00227790432810.4%
 
0.00236406619410.4%
 
0.00317460317510.4%
 
ValueCountFrequency (%) 
0.145645645610.4%
 
0.14211886310.4%
 
0.137992831510.4%
 
0.136448598110.4%
 
0.128672745710.4%
 

AA_FEMALE
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct265
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09746139994
Minimum0
Maximum0.5059101655
Zeros7
Zeros (%)2.6%
Memory size2.1 KiB
2020-11-09T09:23:06.993597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.003269976174
Q10.01092482284
median0.03167731867
Q30.1147580841
95-th percentile0.4485075408
Maximum0.5059101655
Range0.5059101655
Interquartile range (IQR)0.1038332613

Descriptive statistics

Standard deviation0.136320012
Coefficient of variation (CV)1.398707715
Kurtosis1.818536173
Mean0.09746139994
Median Absolute Deviation (MAD)0.02573200705
Skewness1.740154777
Sum26.70442358
Variance0.01858314566
MonotocityNot monotonic
2020-11-09T09:23:07.163596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
072.6%
 
0.0104712041920.7%
 
0.00349650349720.7%
 
0.0109890109920.7%
 
0.0205992509410.4%
 
0.0402298850610.4%
 
0.0628722700210.4%
 
0.184615384610.4%
 
0.00827814569510.4%
 
0.0304990757910.4%
 
Other values (255)25593.1%
 
ValueCountFrequency (%) 
072.6%
 
0.00076161462310.4%
 
0.00142247510710.4%
 
0.00196463654210.4%
 
0.00241545893710.4%
 
ValueCountFrequency (%) 
0.505910165510.4%
 
0.499173553710.4%
 
0.48709122210.4%
 
0.483731019510.4%
 
0.482692307710.4%
 

W_FEMALE
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct270
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.347509595
Minimum0
Maximum0.568627451
Zeros4
Zeros (%)1.5%
Memory size2.1 KiB
2020-11-09T09:23:07.330596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01057688583
Q10.3077527687
median0.400431284
Q30.4444444444
95-th percentile0.4756571742
Maximum0.568627451
Range0.568627451
Interquartile range (IQR)0.1366916758

Descriptive statistics

Standard deviation0.1425755446
Coefficient of variation (CV)0.4102780087
Kurtosis0.5858519064
Mean0.347509595
Median Absolute Deviation (MAD)0.05457110692
Skewness-1.312720513
Sum95.21762902
Variance0.02032778592
MonotocityNot monotonic
2020-11-09T09:23:07.486600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
041.5%
 
0.444444444420.7%
 
0.458333333310.4%
 
0.421112372310.4%
 
0.266409266410.4%
 
0.351648351610.4%
 
0.0366013071910.4%
 
0.0278699402810.4%
 
0.441860465110.4%
 
0.422314049610.4%
 
Other values (260)26094.9%
 
ValueCountFrequency (%) 
041.5%
 
0.00148809523810.4%
 
0.00172117039610.4%
 
0.00330578512410.4%
 
0.00335008375210.4%
 
ValueCountFrequency (%) 
0.56862745110.4%
 
0.53703703710.4%
 
0.519480519510.4%
 
0.511363636410.4%
 
0.494565217410.4%
 

MALE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct270
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5133449215
Minimum0.3657407407
Maximum0.6230769231
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-11-09T09:23:07.670599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.3657407407
5-th percentile0.4744780981
Q10.5004415928
median0.5151807076
Q30.529217079
95-th percentile0.5505850504
Maximum0.6230769231
Range0.2573361823
Interquartile range (IQR)0.02877548616

Descriptive statistics

Standard deviation0.02781375505
Coefficient of variation (CV)0.05418141661
Kurtosis5.813975732
Mean0.5133449215
Median Absolute Deviation (MAD)0.01430102395
Skewness-0.8353810051
Sum140.6565085
Variance0.0007736049702
MonotocityNot monotonic
2020-11-09T09:23:07.845595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.517857142931.1%
 
0.520.7%
 
0.478260869620.7%
 
0.477611940310.4%
 
0.494932432410.4%
 
0.51596053410.4%
 
0.534883720910.4%
 
0.551515151510.4%
 
0.50304506710.4%
 
0.508534850610.4%
 
Other values (260)26094.9%
 
ValueCountFrequency (%) 
0.365740740710.4%
 
0.392156862710.4%
 
0.403100775210.4%
 
0.409090909110.4%
 
0.449225473310.4%
 
ValueCountFrequency (%) 
0.623076923110.4%
 
0.607329842910.4%
 
0.604501607710.4%
 
0.567639257310.4%
 
0.558541266810.4%
 

AA_MALE
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct269
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09633370213
Minimum0
Maximum0.4923076923
Zeros6
Zeros (%)2.2%
Memory size2.1 KiB
2020-11-09T09:23:08.017596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00244639585
Q10.01324664532
median0.0315673677
Q30.1185361989
95-th percentile0.4202412158
Maximum0.4923076923
Range0.4923076923
Interquartile range (IQR)0.1052895536

Descriptive statistics

Standard deviation0.1300508889
Coefficient of variation (CV)1.350004059
Kurtosis1.617592882
Mean0.09633370213
Median Absolute Deviation (MAD)0.02605436016
Skewness1.678505241
Sum26.39543438
Variance0.01691323371
MonotocityNot monotonic
2020-11-09T09:23:08.190595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
062.2%
 
0.00932835820910.4%
 
0.143031784810.4%
 
0.0353741496610.4%
 
0.432399512810.4%
 
0.0801644398810.4%
 
0.0270270270310.4%
 
0.0603248259910.4%
 
0.020202020210.4%
 
0.0501319261210.4%
 
Other values (259)25994.5%
 
ValueCountFrequency (%) 
062.2%
 
0.00146842878110.4%
 
0.00171526586610.4%
 
0.00186915887910.4%
 
0.00201207243510.4%
 
ValueCountFrequency (%) 
0.492307692310.4%
 
0.483731019510.4%
 
0.479905437410.4%
 
0.470779220810.4%
 
0.469262295110.4%
 

W_MALE
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct271
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3723870294
Minimum0
Maximum0.5916230366
Zeros4
Zeros (%)1.5%
Memory size2.1 KiB
2020-11-09T09:23:08.362594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01409107695
Q10.3252798921
median0.4262484301
Q30.4762438546
95-th percentile0.5128214399
Maximum0.5916230366
Range0.5916230366
Interquartile range (IQR)0.1509639625

Descriptive statistics

Standard deviation0.1491323037
Coefficient of variation (CV)0.400476633
Kurtosis0.6767079372
Mean0.3723870294
Median Absolute Deviation (MAD)0.06283740013
Skewness-1.319189683
Sum102.0340461
Variance0.02224044402
MonotocityNot monotonic
2020-11-09T09:23:08.529600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
041.5%
 
0.472222222210.4%
 
0.0366013071910.4%
 
0.497185741110.4%
 
0.466783216810.4%
 
0.380975374210.4%
 
0.00884955752210.4%
 
0.417437252310.4%
 
0.369230769210.4%
 
0.437450199210.4%
 
Other values (261)26195.3%
 
ValueCountFrequency (%) 
041.5%
 
0.00216919739710.4%
 
0.00426829268310.4%
 
0.00487210718610.4%
 
0.00502512562810.4%
 
ValueCountFrequency (%) 
0.591623036610.4%
 
0.552783109410.4%
 
0.546623794210.4%
 
0.540983606610.4%
 
0.528985507210.4%
 

H_MALE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct269
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03471910726
Minimum0
Maximum0.1516516517
Zeros2
Zeros (%)0.7%
Memory size2.1 KiB
2020-11-09T09:23:08.706594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00573019802
Q10.01302910053
median0.02407224087
Q30.04294699012
95-th percentile0.1038768791
Maximum0.1516516517
Range0.1516516517
Interquartile range (IQR)0.02991788959

Descriptive statistics

Standard deviation0.03089980028
Coefficient of variation (CV)0.8899940903
Kurtosis2.178286383
Mean0.03471910726
Median Absolute Deviation (MAD)0.01329585626
Skewness1.606962231
Sum9.51303539
Variance0.0009547976574
MonotocityNot monotonic
2020-11-09T09:23:08.869594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.0227272727331.1%
 
020.7%
 
0.0219780219820.7%
 
0.0202702702720.7%
 
0.0363997352710.4%
 
0.118942731310.4%
 
0.0108303249110.4%
 
0.0127118644110.4%
 
0.0512820512810.4%
 
0.0358773646410.4%
 
Other values (259)25994.5%
 
ValueCountFrequency (%) 
020.7%
 
0.00186046511610.4%
 
0.00191938579710.4%
 
0.00237529691210.4%
 
0.00315457413210.4%
 
ValueCountFrequency (%) 
0.151651651710.4%
 
0.145794392510.4%
 
0.136950904410.4%
 
0.135258358710.4%
 
0.129131437410.4%
 

AA
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct274
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1937951021
Minimum0
Maximum0.9858156028
Zeros1
Zeros (%)0.4%
Memory size2.1 KiB
2020-11-09T09:23:09.042597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.006665047686
Q10.02429001063
median0.06092491054
Q30.2266699516
95-th percentile0.8496860431
Maximum0.9858156028
Range0.9858156028
Interquartile range (IQR)0.202379941

Descriptive statistics

Standard deviation0.2655563184
Coefficient of variation (CV)1.370294273
Kurtosis1.684171124
Mean0.1937951021
Median Absolute Deviation (MAD)0.05097637134
Skewness1.70383473
Sum53.09985797
Variance0.07052015825
MonotocityNot monotonic
2020-11-09T09:23:09.211597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.00694444444410.4%
 
0.0415335463310.4%
 
0.0665866826610.4%
 
0.15201465210.4%
 
0.0358851674610.4%
 
0.310.4%
 
0.131652661110.4%
 
0.00675675675710.4%
 
0.0343878954610.4%
 
0.0598639455810.4%
 
Other values (264)26496.4%
 
ValueCountFrequency (%) 
010.4%
 
0.00146842878110.4%
 
0.00330033003310.4%
 
0.00361010830310.4%
 
0.00383877159310.4%
 
ValueCountFrequency (%) 
0.985815602810.4%
 
0.97510.4%
 
0.96746203910.4%
 
0.945454545510.4%
 
0.942622950810.4%
 

H
Real number (ℝ≥0)

HIGH CORRELATION

Distinct271
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06643599686
Minimum0.003278688525
Maximum0.2972972973
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-11-09T09:23:09.382594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.003278688525
5-th percentile0.01174597142
Q10.0255879455
median0.04615904401
Q30.08572051646
95-th percentile0.1898096288
Maximum0.2972972973
Range0.2940186088
Interquartile range (IQR)0.06013257095

Descriptive statistics

Standard deviation0.05859129269
Coefficient of variation (CV)0.8819208782
Kurtosis2.554858757
Mean0.06643599686
Median Absolute Deviation (MAD)0.02466137016
Skewness1.663912078
Sum18.20346314
Variance0.003432939579
MonotocityNot monotonic
2020-11-09T09:23:09.548597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.0392156862720.7%
 
0.0204081632720.7%
 
0.0270270270320.7%
 
0.0503731343310.4%
 
0.0236486486510.4%
 
0.180613668110.4%
 
0.0568862275410.4%
 
0.129629629610.4%
 
0.0210843373510.4%
 
0.0666032350110.4%
 
Other values (261)26195.3%
 
ValueCountFrequency (%) 
0.00327868852510.4%
 
0.00328947368410.4%
 
0.0057581573910.4%
 
0.00712589073610.4%
 
0.00750469043210.4%
 
ValueCountFrequency (%) 
0.297297297310.4%
 
0.282242990710.4%
 
0.279069767410.4%
 
0.263931104410.4%
 
0.253584229410.4%
 

W
Real number (ℝ≥0)

HIGH CORRELATION

Distinct273
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7198966244
Minimum0.001488095238
Maximum0.9901315789
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-11-09T09:23:09.728596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.001488095238
5-th percentile0.02386058938
Q10.6283667945
median0.8340800387
Q30.9234737521
95-th percentile0.9675899899
Maximum0.9901315789
Range0.9886434837
Interquartile range (IQR)0.2951069576

Descriptive statistics

Standard deviation0.2879347468
Coefficient of variation (CV)0.3999667967
Kurtosis0.75010646
Mean0.7198966244
Median Absolute Deviation (MAD)0.1066515087
Skewness-1.398612485
Sum197.2516751
Variance0.08290641844
MonotocityNot monotonic
2020-11-09T09:23:09.893595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.946428571420.7%
 
0.912313432810.4%
 
0.961111111110.4%
 
0.627565982410.4%
 
0.722811671110.4%
 
0.122887864810.4%
 
0.91122715410.4%
 
0.842227378210.4%
 
0.820359281410.4%
 
0.957382039610.4%
 
Other values (263)26396.0%
 
ValueCountFrequency (%) 
0.00148809523810.4%
 
0.00172117039610.4%
 
0.00216919739710.4%
 
0.00472813238810.4%
 
0.00487210718610.4%
 
ValueCountFrequency (%) 
0.990131578910.4%
 
0.988483685210.4%
 
0.984034833110.4%
 
0.980519480510.4%
 
0.980327868910.4%
 

Expend_per_pupil
Real number (ℝ≥0)

Distinct273
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9582.177774
Minimum5304.33
Maximum18582.45
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-11-09T09:23:10.063596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5304.33
5-th percentile7715.3585
Q18420.035
median9222.975
Q310528.88
95-th percentile12620.0375
Maximum18582.45
Range13278.12
Interquartile range (IQR)2108.845

Descriptive statistics

Standard deviation1727.361673
Coefficient of variation (CV)0.1802681722
Kurtosis4.902725219
Mean9582.177774
Median Absolute Deviation (MAD)921.435
Skewness1.427940412
Sum2625516.71
Variance2983778.348
MonotocityNot monotonic
2020-11-09T09:23:10.243598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5449.5920.7%
 
10789.4310.4%
 
9016.7510.4%
 
8266.1310.4%
 
12407.3710.4%
 
8364.2510.4%
 
8652.2210.4%
 
11513.8510.4%
 
9136.4310.4%
 
8923.3310.4%
 
Other values (263)26396.0%
 
ValueCountFrequency (%) 
5304.3310.4%
 
5449.5920.7%
 
5541.1410.4%
 
6174.9310.4%
 
6949.9910.4%
 
ValueCountFrequency (%) 
18582.4510.4%
 
18237.5810.4%
 
15288.4210.4%
 
14988.8810.4%
 
14322.3810.4%
 

Interactions

2020-11-09T09:22:25.907597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:26.077597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:26.242595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:26.376594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:26.532596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:26.679593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:26.829594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:26.958594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:27.089594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:27.232593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:27.380593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:27.527593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:27.676594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:27.815593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:27.963593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:28.106593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:28.263593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:28.425595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:28.596593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:28.845594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:29.003597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:29.141594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:29.308594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:29.471600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:29.624595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:29.779596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:29.935596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:30.098593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:30.263593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:30.419594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:30.582593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:30.741597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:30.905598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:31.029594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:31.187597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:31.323599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:31.476593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:31.601593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:31.737593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:31.870593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:31.997593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:32.126594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:32.252594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:32.385596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:32.522593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:32.649594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:32.781594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:32.904596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:33.035595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:33.187594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:33.357594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:33.505594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:33.785593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:33.928596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:34.092602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:34.247593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:34.400594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:34.554595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:34.712596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:34.866599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:35.016593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:35.162593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:35.319593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:35.476599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:35.646594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:35.788593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:35.941596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:36.068594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:36.213597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:36.344601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:36.485593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:36.620593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:36.754594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:36.876597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:37.002593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:37.132594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:37.276595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:37.410597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:37.545599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:37.676598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:37.813593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:37.968600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:38.126596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:38.271598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:38.436596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:38.586593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:38.747593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:38.898593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:39.049593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:39.195597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:39.353593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:39.514599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:39.823594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:39.968593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:40.125598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:40.277596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:40.429594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:40.574594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:40.732594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:40.859597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:41.002593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:41.129594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:41.280597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:41.419594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:41.554593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:41.687593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:41.823593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:41.964594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:42.108593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:42.243593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:42.392595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:42.528593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:42.672593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:42.817596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:42.970600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:43.094593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:43.248603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:43.387593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:43.536594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:43.675598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:43.817601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:43.946593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:44.084596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:44.229593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:44.377594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:44.512593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:44.650602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:44.786593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:44.929596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:45.057593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:45.198594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:45.328593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:45.471593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:45.606593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:45.748593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:45.881593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:46.016596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:46.149593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:46.280593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:46.425593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:46.572593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:46.703599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:47.024594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:47.148593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:47.292598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:47.432596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:47.584593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:47.721597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:47.871600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:48.008593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:48.158592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:48.300596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:48.442597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:48.575593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:48.716594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:48.850594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:48.985594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:49.119593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:49.258593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:49.397597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:49.543593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:49.695592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:49.853593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:49.996593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:50.154594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:50.297593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:50.446593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:50.586593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:50.734596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:50.865593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:50.991593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:51.125597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:51.281593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:51.420594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:51.577600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:51.727596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:51.880593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:52.033593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:52.198592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:52.340600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:52.514598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:52.663594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:52.818597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:52.964593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:53.116594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:53.267593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:53.420594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:53.581598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:53.739594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:53.893597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:54.045594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:54.195593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:54.350596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:54.496593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:54.655593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:54.794597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:54.930593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:55.049593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:55.191593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:55.328597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:55.473593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:55.610601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:55.743600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:56.128598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:56.280599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:56.423595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:56.563594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:56.705597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:56.852597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:57.005593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:57.169594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:57.315593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:57.471593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:57.608593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:57.766598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:57.914598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:58.060596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:58.208596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:58.351594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:58.500596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:58.660594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:58.807594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:58.953593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:59.092593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:59.247597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:59.387594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:59.541597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:59.675593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:59.826595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:22:59.962600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:00.112594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:00.253593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:00.391593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:00.523593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:00.657593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:00.800593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:00.937594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:01.063593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:01.201594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:01.339593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:01.482598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:01.630594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:01.791594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:01.939594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:02.093596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:02.235593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:02.394593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:02.541593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:02.686594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:02.830593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:02.973597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:03.109593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:03.264596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:03.411593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:03.558593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:03.707593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-09T09:23:10.415593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-09T09:23:10.725596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-09T09:23:11.011593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-09T09:23:11.314594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-11-09T09:23:04.003593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T09:23:04.407593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexgrad_rateTOTALECONOMICALLY_DISADVANTAGEDFEMALEH_FemaleAA_FEMALEW_FEMALEMALEAA_MALEW_MALEH_MALEAAHWExpend_per_pupil
000.9610750.2474420.4827910.0083720.0027910.4651160.5172090.0074420.5004650.0018600.0102330.0102330.9655819171.37
110.9511200.3392860.4517860.0107140.0205360.4098210.5482140.0196430.5053570.0160710.0401790.0267860.9151799523.76
220.9214060.1998580.4914650.0440970.0704130.3513510.5085350.0853490.3463730.0469420.1557610.0910380.69772412546.31
330.984810.1933470.4428270.0374220.0166320.3825360.5571730.0270270.4823280.0374220.0436590.0748440.8648659106.97
450.915340.2378280.4887640.0561800.0205990.4082400.5112360.0224720.4232210.0599250.0430710.1161050.8314618279.85
560.946370.3265310.4866560.0062790.0109890.4615380.5133440.0282570.4631080.0125590.0392460.0188380.9246479673.19
670.935150.3864080.4485440.0213590.0097090.4155340.5514560.0077670.4893200.0388350.0174760.0601940.90485410027.42
780.9313960.2077360.4935530.0358170.0093120.4376790.5064470.0143270.4491400.0336680.0236390.0694840.8868199268.68
890.9316670.1511700.4997000.0173970.0083980.4691060.5003000.0173970.4571090.0185960.0257950.0359930.9262158945.95
9100.996820.2184750.4692080.0513200.1173020.2815250.5307920.1217010.3460410.0439880.2390030.0953080.62756611480.54

Last rows

df_indexgrad_rateTOTALECONOMICALLY_DISADVANTAGEDFEMALEH_FemaleAA_FEMALEW_FEMALEMALEAA_MALEW_MALEH_MALEAAHWExpend_per_pupil
2643130.981660.0602410.5421690.0361450.0120480.4638550.4578310.0240960.3915660.0240960.0361450.0602410.85542211230.73
2653140.9710820.0323480.4796670.0268020.0304990.3881700.5203330.0378930.4066540.0351200.0683920.0619220.7948248390.27
2663150.9816670.0077980.5176960.0209960.0323940.3713260.4823040.0341930.3491300.0257950.0665870.0467910.7204567977.52
2673160.9615330.0450100.4964120.0430530.0293540.4005220.5035880.0254400.4266140.0358770.0547950.0789300.8271368346.05
2683170.9418950.2474930.4860160.0585750.0781000.3356200.5139840.0796830.3620050.0638520.1577840.1224270.6976257842.51
2693180.9722200.0711710.4878380.0180180.0445950.4072070.5121620.0504500.4202700.0247750.0950450.0427930.8274777519.21
2703190.965710.1856390.4605950.0052540.0210160.4325740.5394050.0350260.4763570.0210160.0560420.0262700.90893210360.40
2713200.9719350.1090440.4739020.0273900.0506460.3757110.5260980.0470280.4304910.0335920.0976740.0609820.8062027995.72
2723240.805200.6826920.4942310.0057690.4826920.0000000.5057690.4923080.0057690.0057690.9750000.0115380.0057699794.46
2733260.604610.7093280.4989150.0086770.4837310.0000000.5010850.4837310.0021690.0151840.9674620.0238610.00216910769.02